Affiliation:
1. School of Electronic Engineering Jiangsu Ocean University Lianyungang China
2. School of Science Jiangsu Ocean University Lianyungang China
3. School of Automation Nanjing University of Science and Technology Nanjing China
Abstract
AbstractThis article focuses on a class of nonstrict feedback systems with input delay, state delays and time‐varying full‐state constraints by proposing an adaptive neural control scheme. To overcome the problems of all state variables effected by time‐varying constraints, the asymmetric time‐varying barrier Lyapunov functions are constructed. The influence of state delays and input delay is eliminated by employing suitable Lyapunov–Krasovskii functionals. Additionally, the process of controller design is based on backstepping method and the unknown functions can be approximated by radial basis function neural networks. Moreover, the problem of repeated differentiations for nonlinear components during controller design is hugely simplified by taking advantage of the dynamic surface control method. The boundness of all the closed‐loop signals can be ensured by the designed controller. Finally, two numerical simulations illustrate that the proposed adaptive neural control scheme is effective.
Funder
China Postdoctoral Science Foundation
National Natural Science Foundation of China
Postdoctoral Science Foundation of Jiangsu Province